| title: “Bahn analysis report” |
| author: “Jinshi” |
| date: “March 26, 2019” |
| output: |
| html_document: |
| df_print: paged |
Global spatial destribution of soil repiration sites
Improve the Rs measure equipment so it can measure Rs in cold condition; Increasing funds; Bahn’s approach [Bahn et al. (2004) Biogeosciences] + Rs measured at annuam mean temperature linearly related with annual Rs rate + Rs at mean temperature: soil respiration measured at annual mean temperature / monthly mean temperature / daily mean temperature
Whether Rs measured at daily mean temperature represent daily mean Rs?
If not what is the mechanism?
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## Warning: Removed 3 rows containing missing values (geom_point).
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##
## Call:
## lm(formula = bahn ~ Rs_annual, data = sdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1269.9 -106.0 18.3 117.3 1222.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.58745 17.22279 -0.266 0.79
## Rs_annual 1.07455 0.01846 58.222 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 233.1 on 821 degrees of freedom
## Multiple R-squared: 0.805, Adjusted R-squared: 0.8048
## F-statistic: 3390 on 1 and 821 DF, p-value: < 2.2e-16
## Tue Mar 26 18:09:52 2019 -------------------+++++-------------------
## Tue Mar 26 18:09:52 2019 How are Rs_annual and Rs_annual_bahn_Temp related?
## Tue Mar 26 18:09:52 2019 sdata rows = 823 cols = 143
## Tue Mar 26 18:09:52 2019 Model summary:
##
## Call:
## lm(formula = temp ~ Rs_annual, data = sdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1269.9 -106.0 18.3 117.3 1222.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.58745 17.22279 -0.266 0.79
## Rs_annual 1.07455 0.01846 58.222 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 233.1 on 821 degrees of freedom
## Multiple R-squared: 0.805, Adjusted R-squared: 0.8048
## F-statistic: 3390 on 1 and 821 DF, p-value: < 2.2e-16
##
## Tue Mar 26 18:09:52 2019 Plotting and saving model diagnostics...
## Tue Mar 26 18:09:52 2019 Plotting and saving model residuals...
## Tue Mar 26 18:09:52 2019 Saving outputs/3-modelresids.pdf
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## Tue Mar 26 18:09:52 2019 Test H0 of intercept=0: p-value = 0.7900293
## Tue Mar 26 18:09:52 2019 Test H0 of slope=1: p-value = 5.863901e-05
## [1] 0.7900293
## [1] 5.863901e-05
##
## Call:
## lm(formula = bahn ~ Rs_annual, data = sdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2450.0 -173.0 -0.3 151.8 4964.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -29.30007 27.53434 -1.064 0.288
## Rs_annual 1.03037 0.02951 34.921 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 372.7 on 821 degrees of freedom
## Multiple R-squared: 0.5976, Adjusted R-squared: 0.5971
## F-statistic: 1219 on 1 and 821 DF, p-value: < 2.2e-16
## Tue Mar 26 18:09:53 2019 -------------------+++++-------------------
## Tue Mar 26 18:09:53 2019 How are Rs_annual and Rs_annual_bahn_Temp related?
## Tue Mar 26 18:09:53 2019 sdata rows = 823 cols = 143
## Tue Mar 26 18:09:53 2019 Model summary:
##
## Call:
## lm(formula = temp ~ Rs_annual, data = sdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2450.0 -173.0 -0.3 151.8 4964.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -29.30007 27.53434 -1.064 0.288
## Rs_annual 1.03037 0.02951 34.921 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 372.7 on 821 degrees of freedom
## Multiple R-squared: 0.5976, Adjusted R-squared: 0.5971
## F-statistic: 1219 on 1 and 821 DF, p-value: < 2.2e-16
##
## Tue Mar 26 18:09:53 2019 Plotting and saving model diagnostics...
## Tue Mar 26 18:09:53 2019 Plotting and saving model residuals...
## Tue Mar 26 18:09:53 2019 Saving outputs/3-modelresids.pdf
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## Tue Mar 26 18:09:53 2019 Test H0 of intercept=0: p-value = 0.2875834
## Tue Mar 26 18:09:53 2019 Test H0 of slope=1: p-value = 0.3037032
## [1] 0.2875834
## [1] 0.3037032
##
## Call:
## lm(formula = bahn ~ Rs_annual, data = sdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2356.27 -167.25 -1.34 142.82 2746.39
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -32.36603 24.27741 -1.333 0.183
## Rs_annual 1.00324 0.02602 38.563 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 328.6 on 821 degrees of freedom
## Multiple R-squared: 0.6443, Adjusted R-squared: 0.6439
## F-statistic: 1487 on 1 and 821 DF, p-value: < 2.2e-16
## Tue Mar 26 18:09:54 2019 -------------------+++++-------------------
## Tue Mar 26 18:09:54 2019 How are Rs_annual and Rs_annual_bahn_Temp related?
## Tue Mar 26 18:09:54 2019 sdata rows = 823 cols = 143
## Tue Mar 26 18:09:54 2019 Model summary:
##
## Call:
## lm(formula = temp ~ Rs_annual, data = sdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2356.27 -167.25 -1.34 142.82 2746.39
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -32.36603 24.27741 -1.333 0.183
## Rs_annual 1.00324 0.02602 38.563 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 328.6 on 821 degrees of freedom
## Multiple R-squared: 0.6443, Adjusted R-squared: 0.6439
## F-statistic: 1487 on 1 and 821 DF, p-value: < 2.2e-16
##
## Tue Mar 26 18:09:54 2019 Plotting and saving model diagnostics...
## Tue Mar 26 18:09:54 2019 Plotting and saving model residuals...
## Tue Mar 26 18:09:54 2019 Saving outputs/3-modelresids.pdf
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## Tue Mar 26 18:09:54 2019 Test H0 of intercept=0: p-value = 0.1828444
## Tue Mar 26 18:09:54 2019 Test H0 of slope=1: p-value = 0.9008364
## [1] 0.1828444
## [1] 0.9008364
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## Warning: Removed 510 rows containing missing values (geom_errorbarh).
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## Warning: Removed 495 rows containing missing values (geom_errorbarh).
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## Warning: Removed 495 rows containing missing values (geom_errorbarh).
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## Warning: Removed 8 rows containing missing values (geom_errorbarh).
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## Warning: Removed 495 rows containing missing values (geom_errorbarh).
## Warning in qt((1 - level)/2, df): NaNs produced
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## Warning in qt((1 - level)/2, df): NaNs produced
## Warning in qt((1 - level)/2, df): Removed 2 rows containing missing values
## (geom_errorbarh).
## Warning: Removed 454 rows containing missing values (geom_errorbarh).
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## Warning: Removed 454 rows containing missing values (geom_errorbarh).
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## Warning: Removed 57 rows containing missing values (geom_errorbarh).
srdb_mat <- subset( srdb, TAIR_LTM_dev <= 2 )
srdb_mat2 <- subset( srdb, TAIR_dev <= 2 )
## Warning: Removed 187 rows containing missing values (geom_errorbarh).
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## Warning: Removed 187 rows containing missing values (geom_errorbarh).
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## Tue Mar 26 18:10:10 2019 -------------------+++++-------------------
## Tue Mar 26 18:10:10 2019 Bahn relationship for these data:
##
## Call:
## lm(formula = Rs_annual ~ Rs_TAIR, data = sdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -746.75 -111.49 -39.62 86.43 1273.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 160.045 13.255 12.07 <2e-16 ***
## Rs_TAIR 344.963 5.926 58.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 194.7 on 821 degrees of freedom
## Multiple R-squared: 0.8049, Adjusted R-squared: 0.8047
## F-statistic: 3388 on 1 and 821 DF, p-value: < 2.2e-16
1 T&Drought function (Maybe use PDSI) 2 seprete out Agriculture & Wetland 3 Using SD information with boosting? 4 Use Rs_mat predict Rh? 5 Use this approach estimate global Rs 6 Think about application
subtest_1(5227)
## T a b c d Model_output_units Model_type
## 451 14.2 0.573 0.0924 0 NA umol CO2/m2/s Exponential, R=a exp(b(T-c))
## Record_number Study_number Rs_annual Rs_annual_bahn Rs_TAIR_units
## 451 3430 5227 722.26 696.5665 1.524751